ANOPA: 'Statistical' Systematics for Young-Earth Creationists

ARTICLE ANOPA: ‘Statistical’ Systematics for Young-Earth Creationists Dan Bolnick, University of Texas, Austin reationists has been working hard to make been active members in the Baraminology Study Ctheir views appear as legitimate science. Part Group, whose website proclaims its “ultimate goal is of this strategy has been to get papers pub- to develop origin models that accommodate empiri- lished in peer-reviewed scientific journals. cal data in a biblical framework of earth history Creationists have used two strategies to achieve this through scientifically sound analysis of biological data goal. First, the recent review paper on “intelligent and scholarly analysis of biblical texts” design” (Meyer 2004) in the Proceedings of the (<http://www.bryancore.org/bsg/aboutmain.html>). Biological Society of Washington was published by To this end, Cavanaugh has been developing a bypassing the normal peer review process.According quantitative method for identifying whether a collec- to a recent statement from the Council that publishes tion of species represent a single “created kind”or are the journal, the editor Richard Sternberg handled the sufficiently distinct as to qualify as members of differ- paper in a manner “contrary to typical editorial prac- ent holobaramins. This method,called ‘Analysis of tices”(<http://www.biolsocwash.org/>). Pattern’(ANOPA) is touted as a method to “reduce the The second approach is to sanitize the content of dimensionality of multi-dimensional data with mini- the paper. Two other papers written by creationists mal loss of information and no assumptions about the have been published in peer reviewed journals in data’s distribution”(Wood and Cavanaugh 2003: 2). By 2004 (Behe and Snoke 2004; Cavanaugh and reducing complex multivariate data down to 3 dimen- Sternberg 2004). Unlike Meyer’s paper, which was sions,ANOPA allows the user to visualize patterns of handled by a friendly editor who retired soon after similarity and difference among species to see if they (and also co-authored the Cavanaugh and Sternberg fall into discrete clusters. This purely descriptive paper), these have actually passed through the peer method could then be combined with statistical infer- review process.This was accomplished by removing ence (confidence intervals around the clusters) to any overt reference to creationism or “intelligent infer whether these clusters might overlap.The impli- design”, a strategy clearly outlined in a recent paper cation is that overlap would imply membership in a by a group of young-earth creationists: common holobaramin, and disjunct groups imply separate baramins. To make a creationist theory ‘theory neutral’ As of late 2004, three papers had been published (that is palatable to non-creationists), much of using ANOPA. The first two were published in cre- what makes it distinctly creationist must be ationist journals (Origins, and Occasional Papers of removed. This may be useful, for example, in the Baraminology Study Group) and clearly apply order to get a controversial scientific study ANOPA as a criterion for evaluating whether groups reviewed by competent evolutionary scholars of species (subtribe Flaveriinae and tribe Heliantheae, in a secular journal, but the elimination of cre- respectively) represent one or more of the Bible’s creationist content as a general practice generates ated kinds (Cavanaugh and Wood 2002; Wood and more work for creationists.To integrate ‘theory- Cavanaugh 2001). Both papers provide only the neutral’ research back into the creation model sketchiest description of ANOPA as a method, citing from which it came, all that was excised must an unpublished paper.Without full description of the be replaced (Wood and others 2003). method, it was impossible to evaluate their claims Such excision is a clever, if intellectually dishonest, about its value, such as: “Because these calculations ploy that underlies the recent publication of a paper require no assumptions about the distribution of the by the young-earth creationist David Cavanaugh and data and retain more information regarding dataset the biologist Richard Sternberg in the peer-reviewed variation, ANOPA can reveal patterns obscured by Journal of Biological Systems. Both authors have other variance-analysis methods such as Principle Component Analysis. Consequently,ANOPA is the best Dk is Dan Bolnick is Dan Bolnick is Dan Bolnick is Dan available method to display biological character Bolnick is Dan Bolnick is Dan Bolnick is Dan Bolnick is space and reveal taxonomic patterns.” (Wood and JUL-AUG 2006 Dan Bolnick is Dan Bolnick is Dan Bolnick is Dan Cavanaugh 2003, emphasis added). REPORTS Bolnick is Dan Bolnick is Dan Bolnick is Dan Bolnick is A complete description of ANOPA has finally Dan Bolnick is Dan Bolnick is Dan become available, in a peer-reviewed journal. Taking 1 Continued on page 27 Continued from page 22 the strategy outlined in Wood and others (2003) to get through peer review,Cavanaugh and Sternberg wrote FIGURE 1. a paper describing ANOPA—a method developed for A) Rotation of axes by Principle young-earth creationist purposes and used as such in Componenet other venues,— but presented here without any ref- Analysis. erence to creationism. Cavanaugh and Sternberg B) Rotation via (2004) also apply ANOPA to a group of well known ANOPA, connect- North American freshwater fishes, the Centrarchidae ing the centroid (sunfish, bass, crappies, fliers), for which a large data to the most most set of morphological traits was available. distant data point to form This publication raises an interesting strategic the primary axis. question: how should the scientific community han- Location/choice dle papers written by creationists, describing meth- of outgroup will have a major ods or ideas designed to support creationist research, effect on results. that make no overt reference to creationism? One option is to treat such papers the way we treat any scientific work: assess the strength of its methods, and two such dimensions for clarity (Figure 2A).There are the rigor of its interpretation. Therefore, the remain- two conceptually distinct versions of ANOPA: 1- der of this essay will be spent describing and cri- dimensional ANOPA draws a histogram of distances tiquing ANOPA, and its particular application by among points, while 2- or 3-dimensional ANOPA Cavanaugh and Sternberg (C&S). reduces the data down to three variables and pro- HOW IS ANOPA SUPPOSED TO WORK? duces a scatter plot of the data points to look for dis- Multivariate data are a common feature of studies that junct groups. try to classify species based on morphological simi- For 1-D ANOPA, we first identify the central point larities. Such data are difficult to visualize because we (‘centroid’) in the cloud of data, the average of each cannot simultaneously view relationships among trait across all species (Figure 2B). We then calculate more than three variables at a time. Statisticians have the Euclidian length of the vector connecting each gotten around this problem with a variety of methods species’ point to that centroid (Figure 2C). Looking at such as Principle Component Analysis (PCA) that try a histogram of these distances (Figure 2D), C&S sug- to reduce the variation in many different variables, gest that different peaks in the histogram correspond down into a smaller number of important variables to different subgroups. In fact, these results can arise that capture most of the action.This is best illustrated from random chance: the data in Figure 2D appear to in Figure 1, where we have hypothetical data on the have 3 groups, but this is an artifact of small sample number of vertebrae and gill rakers for each of 20 sizes drawn from a single normal distribution. One’s species. There is clearly important variation in both choice of how wide the histogram bars are will affect traits, so we do not want to ignore one of them. We resolution and may create statistically non-significant can get around this problem by ‘rotating’ the axis of groups, or can obscure real groups.The 1-D test can the graph so that one axis (the ‘first principle compo- also fail to identify distinct groups (Figure 3), indicat- nent’) captures most of the action. We could then ing that it is likely to be very sensitive to both overall either ignore the remaining variation, or use a second principle component axis that is perpendicular to the first, to “explain” the remaining variation — by show- ing us how strong a relationship there is between individual variables and these axes. If we have 10 variables instead of the 2 illustrated in Figure 1, there will be 10 possible Principle Components (PCs), but in general we find that the first couple of PCs capture FIGURE 2. A) Each species has values for each B) Calculate centroid of data most of the meaningful information. of m different characters.The goal is to (X0), which is the average for ANOPA tries to do something very similar,reducing reduce these m-dimensions to 2 or 3 each variable. some large number of variables down to three axes of dimensions to visualize the distribution variation that are meant to capture any meaningful of points to look for separate clouds of points.While this example uses just 2 patterns in the data (Cavanaugh and Sternberg 2004). dimensions, ANOPA works for more The algorithm for ANOPA is simple, and is summa- dimensions. rized in Figure 2. The method assumes we have a standard data set for cladistic analysis. That is, each species is given some value for each of a number of different traits. These traits can be continuous (for example, body size), ordinal (for example, number of vertebrae), or categorical (for example, presence or absence of a C) D) Calculate distance (a0) Draw histogram of these trait; or a number of different character states).

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